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CivitAI 🏆 Style Fusion - FFusionAI Entry (+dataset)

Surrealist art <lora:Book_FFusion:1> <lora:Book_FFusion_32:0.21> <lora:FF-Style-Dall-e-FFusion-Surrealism:0.3> a book with a mountain in the middle of it, 3 d epic illustrations, 3 d illutration, detailed digital 3d art, intricate 3 d illustration, trend on behance 3d art, trend on behance 3 d art, 3d digital art 4k, cool 3d visualisation, 3 d digital art, 3d digital art . Dreamlike, mysterious, provocative, symbolic, intricate, detailed
Surrealist art <lora:Book_FFusion:1> <lora:Book_FFusion_32:0.21> <lora:FF-Style-Dall-e-FFusion-Surrealism:0.3> minecraft notebook with a 3d island on it, 3 d epic illustrations, isometric voxel art, isometric art, isometric island in the sky, isometric 3d fantasy island, high quality voxel art, intricate 3 d illustration, detailed digital 3d art, voxel art, 3d matte illustration, isometric 3d fantasy, very detailed isometric . Dreamlike, mysterious, provocative, symbolic, intricate, detailed
Surrealist art <lora:Book_FFusion:1> <lora:Book_FFusion_32:0.21> <lora:FF-Style-Dall-e-FFusion-Surrealism:0.3> a notebook with a waterfall and a waterfall, 3 d epic illustrations, isometric art, highly detailed illustration, 3 d illutration, isometric illustration, beautifully illustrated, floating waterfalls, 3d matte illustration, amazing depth, isometric illustration fun, cute detailed artwork, detailed painting 4 k, realistic 3 d style, stunning digital illustration, fabulous illustrations . Dreamlike, mysterious, provocative, symbolic, intricate, detailed
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Model Description by Creator

CivitAI Style Fusion🏆LoRAs

Last update: 🚀 CivitAI Lora5 32DIM Notebook with dataset

Last update: 🚀 CivitAI Lora3 Configuration - Trained with CivitAI Trainer

🚀 Date: 2023-11-10 | Title: CivitAI_64_ALL

🔍 Key Specifications:

  • Resolution: 1024x1024

  • Architecture: stable-diffusion-xl-v1-base/lora

  • Network Dim/Rank: 64.0, Alpha: 1.0

  • Module: networks.lora

  • Learning Rates: UNet LR & TE LR set to optimal levels

  • Optimizer: Advanced AdamW8bit

  • Epochs & Training: Intensive 10 epochs with 576 batches

📊 Model Stats:

  • UNet Weight: Mag - 7.602, Str - 0.0187

Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 64.0 Alpha: 1.0
Module: networks.lora
Learning Rate (LR): 0.0005 UNet LR: 0.0005 TE LR: 5e-05
Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)
Scheduler: constant  Warmup steps: 0
Epoch: 10 Batches per epoch: 576 Gradient accumulation steps: 1
Train images: 2304 Regularization images: 0
Multires noise iterations: 6.0 Multires noise discount: 0.3
Min SNR gamma: 5.0 Zero terminal SNR: True Max grad norm: 1.0  Clip skip: 1
Dataset dirs: 1
        [img] 576 images
UNet weight average magnitude: 7.602270778898858
UNet weight average strength: 0.018722912685324843
Text Encoder (1) weight average magnitude: 2.7649271326702607
Text Encoder (1) weight average strength: 0.009535635958680934
Text Encoder (2) weight average magnitude: 2.6905091182810352
Text Encoder (2) weight average strength: 0.007233532415344915

Delve into FFusionAI's approach to AI-driven style synthesis with our newly released LoRA models. Each model has been developed using CivitAI's official trainer, ensuring precision and quality.

🛠️ LoRA Model Overview:

  • LoRA 1 - Lite Version: Designed for quick testing, this model utilizes a small dataset for swift style generation, operating with a 32-dimension capacity.

  • LoRA 2 - Community Fusion: A robust model developed from over 500+ images, submitted by various users for the CivitAI contest. This iteration also features a 32-dimension capacity.

  • LoRA 3 - Enhanced Fidelity: Building upon LoRA 2, this model is further trained with higher dimensions, focusing on improving the overall image quality.

  • LoRA 4 - Comprehensive Style Mash: Our expansive dataset of 1400 images represents a confluence of all FFusionAI submissions. This model undergoes additional UNet training to refine and diversify the generated styles.

1. FFusionAI Style Capture & Fusion Showdown LoRA

🎨 Dataset and Training:

Included within the package are curated collections accessible at CivitAI Collections. The training prompts have been crafted with BLIP-2, FLAN-T5-XL, and ViT-H-14.

Please note, original prompts were not utilized for training. Instead, intentional modifications were made using blip2-flan-t5-xl & ViT-H-14/laion2b_s32b_b79k to adjust and enhance the training dataset, which can be reviewed here.

🔄 Further Information:

For a detailed examination of the training datasets, parameters, and model specifications, professionals and enthusiasts are encouraged to explore the metadata provided within the collection.

  • LORA 2

    🚀 CivitAI Configuration Overview - 2023-11-10

🚀 Trained with the Official CivitAI Trainer

📅 Date: 2023-11-10

🖼️ Title: CivitAI_ALL

🔍 Resolution: 1024x1024

🏗️ Architecture: stable-diffusion-xl-v1-base/lora

⚙️ Key Settings:

  • Network Dim/Rank: 32.0

  • Alpha: 1.0

  • Module: networks.lora

  • Learning Rates: UNet LR - 0.0005, TE LR - 5e-05

  • Optimizer: AdamW8bit (weight_decay=0.1)

  • Epochs & Batches: 10 epochs, 167 batches/epoch

  • Train Images: 576

📊 Model Stats:

  • UNet Weight: Mag - 3.755, Str - 0.0135

  • Text Encoder (1): Mag - 1.833, Str - 0.0091

  • Text Encoder (2): Mag - 1.836, Str - 0.0071

🏷️ Prominent Tags:

  • Fusion styles, Artgerm, Beeple

  • Dark fantasy, Official artwork, Pinup art

  • Digital illustration, Fantasy & Sci-fi

  • ...and over 4500 more!

🌐 FFusion.ai Contact Information

Proudly maintained by Source Code Bulgaria Ltd & Black Swan Technologies.

  • 📧 For collaborations, inquiries, or support: [email protected]

  • 🌍 Locations: Sofia | Istanbul | London

Connect with Us:

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